Monitoring treatment of colorectal cancer patients with drugs targeting EGFR pathway using mass spectrometry of patient samples

ABSTRACT

Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a non-small-cell lung cancer patient, head and neck squamous cell carcinoma or colorectal cancer patient has likely developed a non-responsiveness to treatment with a drug targeting an epidermal growth factor receptor pathway. As the methods of this disclosure require only simple blood samples, the methods enable a fast and non-intrusive way of measuring when drugs targeting the EGFR pathway cease to be effective in certain patients. This discovery represents the first known example of true personalized selection of these types of cancer patients for treatment using these classes of drugs not only initially, but during the course of treatment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.12/931,324 filed Jan. 27, 2011, which is a divisional of U.S.application Ser. No. 12/584,594 filed Sep. 8, 2009, which is acontinuation-in-part of our prior U.S. patent application Ser. No.11/396,328 filed Mar. 31, 2006, now U.S. Pat. No. 7,736,905. The entirecontent of the '905 patent is incorporated by reference herein.

U.S. patent application Ser. No. 12/584,594 claims priority benefit toU.S. Provisional Application Ser. No. 61/191,927 filed on Sep. 12, 2008,which is also incorporated by reference herein.

U.S. patent application Ser. No. 12/584,594 is also acontinuation-in-part of U.S. patent application Ser. Nos. 12/321,392,12/321,393 and 12/321,394, each filed on Jan. 20, 2009, the content ofeach of which is incorporated by reference herein.

BACKGROUND

This invention relates to the field of monitoring of treatment of cancerpatients with drugs targeting the epidermal growth factor receptor(EGFR) pathway. The monitoring involves mass spectral analysis of bloodsamples from the patient in conjunction with a classification algorithmusing a training set of class-labeled spectra from other patients withthe disease.

Non-Small-Cell Lung Cancer (NSCLC) is a leading cause of death fromcancer in both men and women in the United States. There are at leastfour (4) distinct types of NSCLC, including adenocarcinoma, squamouscell, large cell, and bronchoalveolar carcinoma. Squamous cell(epidermoid) carcinoma of the lung is a microscopic type of cancer mostfrequently related to smoking. Adenocarcinoma of the lung accounts forover 50% of all lung cancer cases in the U.S. This cancer is more commonin women and is still the most frequent type seen in non-smokers. Largecell carcinoma, especially those with neuroendocrine features, iscommonly associated with spread of tumors to the brain. When NSCLCenters the blood stream, it can spread to distant sites such as theliver, bones, brain, and other places in the lung.

Treatment of NSCLC has been relatively poor over the years.Chemotherapy, the mainstay treatment of advanced cancers, is onlymarginally effective, with the exception of localized cancers. Whilesurgery is the most potentially curative therapeutic option for NSCLC,it is not always possible depending on the stage of the cancer.

Recent approaches for developing anti-cancer drugs to treat the NSCLCpatient focus on reducing or eliminating the ability for cancer cells togrow and divide. These anti-cancer drugs are used to disrupt the signalsto the cells to tell them whether to grow or die. Normally, cell growthis tightly controlled by the signals that the cells receive. In cancer,however, this signaling goes wrong and the cells continue to grow anddivide in an uncontrollable fashion, thereby forming a tumor. One ofthese signaling pathways begins when a chemical in the body, calledepidermal growth factor, binds to a receptor that is found on thesurface of many cells in the body. The receptor, known as the epidermalgrowth factor receptor (EGFR) sends signals to the cells, through theactivation of an enzyme called tyrosine kinase (TK) that is found withinthe cells. The signals are used to notify cells to grow and divide.

The use of targeted therapies in oncology has opened new opportunitiesto improve treatment options in advanced stage solid tumors wherechemotherapy was previously the only viable option. For example, drugstargeting the epidermal growth factor receptor (EGFR) pathway (includingwithout limitation, Tarceva (erlotinib), Erbitux (cetuximab), Iressa(gefitinib)) have been approved or are in evaluation for treatment ofadvanced stage solid tumors in particular non-small cell lung cancer(NSCLC). Metro G et al, Rev Recent Clin Trials. 2006 January; 1(1):1-13.

While in some trials EGFR-Inhibitors (EGFR-I) such as those mentionedabove have been shown to generate sufficient survival benefit even inunselected populations, in others there was no substantial benefit. Thislead AstraZeneca to withdraw their EGFR-tyrosine kinase inhibitor (TKI)(gefitinib, Iressa) from the United States market. Even in the case ofapproved EGFR-Is it has become more and more clear that efficient andreliable tests are necessary to identify those patients that mightbenefit from treatment with EGFR-Is vs. those that are not likely tobenefit. Ladanyi M, et al., Mod Pathol. 2008 May; 21 Suppl 2:S16-22. Inour U.S. patent application Ser. No. 11/396,328 we have shown that asimple serum-based pre-treatment test using mass spectrometry andsophisticated data analysis techniques using a classifier and a trainingset of class-labeled spectra from other patients with the disease can beused for patient selection for treatment with drugs targeting the EGFRpathway in non-small cell lung cancer patients. See also Taguchi F. etal, JNCI-2007 v99(11), 838-846. The test, called VeriStrat in itscommercial version, assigns the label “VeriStrat good” or “VeriStratpoor” to pre-treatment serum or plasma samples. It has been shown in theJNCI paper that “VeriStrat good” patients are more likely to benefitfrom EGFR-I treatment than VeriStrat poor patients with a hazard ratioof “VeriStrat good” vs. “VeriStrat poor” patients of approximately 0.5.

There is increasing evidence that the tumors of some patients developresistance to EGFR inhibitors (EGFR-Is) during treatment, even if thetreatment was initially successful as measured by RECIST responsecriteria. Engelman J A, et al. Clin Cancer Res. 2008 May 15;14(10):2895-9. It is unfortunate that currently the only quantitativeway to assess tumor growth is by imaging techniques like x-rays or morepreferably CT imaging. These are typically scheduled at least one monthapart, lead to increases in cumulative radiation dose, and requirehospitals visits. Also there is at least some doubt about thecorrelation of tumor growth measured by CT and eventual outcome measuredby overall survival.

SUMMARY OF THE INVENTION

We have discovered that the methods of mass spectral analysis of patientsamples and classification using a training set described in our priorpatent application provide not only a selection tool for initiallyidentifying patients as being likely to benefit from drugs targeting theEGFR pathway, but also that it is possible to correlate changes in theclassification label for a patient's samples over time with theoccurrence of disease progression. As the methods of this disclosurerequire only simple blood samples, the methods enable a fast andnon-intrusive way of measuring when drugs targeting the EGFR pathwaycease to be effective in certain patients during the course oftreatment. This discovery represents the first known example of truepersonalized selection of NSCLC patients for treatment using theseclasses of drugs not only initially, but during the course of thetreatment.

We have further discovered that the methods of this disclosure also areeffective for personalized selection of other kinds of cancer patientsfor treatment using drugs targeting the EGFR pathway initially andduring the course of the treatment. Specifically, the methods of thisdisclosure are effective for monitoring treatment for head and necksquamous cell carcinoma (HNSCC) and colorectal cancer (CRC) patients.

In one specific embodiment, a method is disclosed of determining whethera NSCLC, HNSCC or CRC patient has developed non-responsiveness totreatment with a drug targeting the EGFR pathway (e.g, an EGFR-I suchTarceva (erlotinib), Erbitux (cetuximab), Iressa (gefitinib), orequivalent) comprising the steps of:

a) obtaining a mass spectrum from a blood-based sample from the patient;

b) performing one or more predefined pre-processing steps on the massspectrum obtained in step a);

c) obtaining integrated intensity values of selected features in saidspectrum at one or more predefined m/z ranges after the pre-processingsteps on the mass spectrum in step b) have been performed;

d) using the values obtained in step c) in a classification algorithmusing a training set comprising class-labeled spectra produced fromblood-based samples from other patients to identify the patient ashaving developed a non-responsiveness to treatment with the said drug.

The method and steps a)-d) are preferably performed periodically overthe course of treatment, e.g., every 30 or 60 days.

Method steps b), c) and d) can be implemented in a general purposecomputer programmed to perform the one or more predefined pre-processingsteps (e.g., background subtraction, spectral alignment andnormalization), the obtaining of the integrated intensity values ofselected features in the spectrum, and the application of theclassification algorithm to the obtained values using the training setof class-labeled spectra (e.g., using a k-nearest neighborclassification algorithm).

In another aspect, a method is disclosed of facilitating treatment of aNSCLC, HNSCC or CRC patient, comprising the steps of:

1) assigning a baseline class label for the patient using a classifieroperating on integrated intensity values at pre-defined m/z ranges inmass-spectral data obtained from a blood-based sample of the patient anda training set comprising class-labeled spectra from other patients, thebaseline class label indicating whether the patient is likely to benefitfrom administration of a drug targeting an epidermal growth factorreceptor pathway. If the baseline class label is “good” (or theequivalent), the patient would be likely to benefit and the drug isadministered to the patient; and

2) thereafter, while the patient is being treated with the drug, a)obtaining a mass spectrum of a blood-based sample of the patient, b)performing one or more predefined pre-processing steps on the massspectrum, c) obtaining integrated intensity values of selected featuresin said spectrum at one or more predefined m/z ranges after thepre-processing steps on the mass spectrum have been performed, and d)assigning a further class label for the sample using the classifier andthe training set, the further class label indicating whether the patienthas developed a non-responsiveness to the treatment with the said drug.This step 2) is preferably performed periodically over the course oftreatment, e.g., every 30 or 60 days.

Method steps 1) and 2) can be computer-implemented by a general purposecomputer that is programmed to receive mass spectral data from theblood-based sample of the patient, stores the training set data, and isprogrammed to perform the predefined pre-processing steps, the obtainingof the integrated intensity values of selected features in the spectrumand conduct the classification algorithm on the sample and the trainingset, e.g., using a k-nearest neighbor classification algorithm tothereby assign a further class label to the sample.

The methods of this disclosure can be performed by a service providerthat receives blood-based samples, generates the initial class label andthe further class label, and then provides the initial and further classlabels to requesting hospital, clinic or physician treating the patient.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph plotting the changes in classification labels for 111NSCLC patients (arranged along the vertical axis) at different times,with time plotted in days along the horizontal axis. The time toprogression (“TTP”) of the disease for each of the patients is indicatedby the square symbol.

FIG. 2 is a graph plotting the changes in classification labels for 111patients (arranged along the vertical axis) at different times, withtime plotted in days along the horizontal axis. The overall survival(“OS”) for each of the patients is indicated by the square symbol.

FIG. 3 is a flow chart showing a method for monitoring a patient inaccordance with a preferred embodiment of this invention.

DETAILED DESCRIPTION

This disclosure will describe an example of methods for patientselection for drugs targeting the EGFR pathway during the course oftreatment wherein the patient is a NSCLC patient. We have discoveredthat the methods are equally applicable to CRC and HNSCC cancer patientsas well.

In order to follow changes in the class labels assigned to a patientduring the course of treatment using the above-described classificationmethods described in our prior patent application U.S. 2007/0231921, oneneeds blood-based samples from the patient taken before treatment, andat reasonable intervals during treatment, e.g., every 30 days, 60 daysor 100 days. In the study described below, these samples were availableon a subset of the samples used in the Taguchi et al. publication. For111 patients serum was collected at baseline, after one month oftherapy, and afterwards every two months until progression or death.

The population characteristics of these samples are shown in Table 1. Atprogression we had 109 complete sets of patients attributes (attributeswere not available for two patients). All patients were treated with theEGFR-TKI gefitinib monotherapy following the baseline serum collection.The median time-to-progression was 3.4 months and the median overallsurvival (from baseline) was 8.3 months. Two patients were still aliveat the close of the study.

TABLE 1 Summary population characteristics for the patients used in thisstudy Variable Value Total enrolled 111 Median age (range), years  68(36-0) Sex (male/female)  86 (77%)/25 (23%) Smoking history (ever vsnever-smokers)  88 (84%)/17 (16%) PS 0/1/2  46 (41%)/46 (41%)/19 (18%)Histological classification Adenocarcinoma  55 (50%) NSCLC  23 (21%)Squamous cell carcinoma  26 (23%) BAL   7 (6%) TNM classification StageIB/IIB/IIIA   5 (5%) Stage IIIB  14 (13%) Stage IV  92 (82%) Medianmonths on treatment (range) 3.5 (0.7-47)

Methods

Mass spectra were generated at Hospitale San Raffaele following theprocedure described in our prior patent application patent publicationNo. 2007/0239121. Upon receipt of these spectra we analyzed them usingthe pre-processing, feature extraction, and classifier using K-nearestneighbor classification algorithm and the training set as described inour prior patent application. These methods are set forth in detail inU.S. patent publication No. 2007/0231921, which is incorporated byreference herein, and therefore a detailed explanation is not set forthfor the sake of brevity. The methods are summarized below.

The resulting labels generated by the classifier (“good”, “poor” and“undefined”) were correlated with time-to-progression and overallsurvival data using standard statistical methods using GraphPad Prismsoftware (GraphPad Software, La Jolla Calif.). In this discussion, aclass label of a serum sample which is classified by the classifier as“good” indicates that a patient is likely to continue to benefit fromtreatment with drugs targeting the EGFR pathway, whereas a class labelof a serum sample which is classified by the classifier as “poor”indicates that a patient is likely developing emerging tumor resistanceto these classes of drugs.

Results

Classification labels produced by the classifier for patient serumsamples at all available time points are shown in FIG. 1. Serum samplesthat were classified as “good” are drawn as triangles, samples that wereclassified as “poor” are labeled as diamonds, and samples for which theclassifier returned a result of “undefined” are labeled as solidsquares. The patients are ordered vertically along the Y axis by theirbaseline label, with the 33 patients with a baseline “poor” grouped atthe bottom, the 76 patients with a baseline “good” grouped above them,and the two baseline “undefined” patients located at the top of the Yaxis. The class labels for a given patient, and changes they exhibitover time, can be observed in FIG. 1 by reading horizontally from agiven point on the Y axis across the Figure from left to right. Forexample, the sequence of symbols 100A, 100B and 100C denote the classlabels assigned to three different patients at times measured along theX (time) axis.

FIG. 1 also shows by the white square symbol the progression times (timeto progression, TTP).

FIG. 1 shows that in the vast majority of cases that patients thatexhibit a “good” label at baseline remained “good” until progression(TTP), and those that exhibited a “poor” at baseline remained pooroverall. There is also a visible change of “good” patients to “poor” or“undefined” which is correlated with the time of progression. Thesechanges are summarized in Table 2.

TABLE 2 A summary of changes of classification label for serum samplesfrom “good” (at baseline) to “poor” or undefined at progression. Totaln. At disease progression Baseline 111 Veristrat+ Veristrat− UndefinedVeriStrat + (good) 76 (68%) 48 (64%) 22 (29%) 5 VeriStrat − (poor) 33(30%)  4 (13%) 28 (87%) 1 Undefined 2 (2%) 2 0 0 Note: data was missingfor one patient at disease progression.A statistical analysis using a chi-squared test showed that thesechanges are statistically significant with a p-value of 0.01 (using thenull-hypothesis that these changes arise by chance).

FIG. 2 is a graph plotting class labels for serum samples of allpatients and all time points together with the measured overall survival(OS). Serum samples that produced a class label “good” are drawn astriangles, those that were classified as poor are drawn as diamonds, andthose that were classified as “undefined” are drawn in as solid squares.The patients are ordered in the vertical axis by their baseline classlabel. Class labels for a given patient over time are observed byreading across the graph to the right. The univariate analysis confirmsthat the baseline “good” and baseline “poor” labels providestatistically significant separation of Kaplan-Meier survival curves forthe “good at baseline” and “poor at baseline” groups, that waspreviously observed.

To further elucidate which patients are changing from “good” to “poor”at progression we performed a subgroup analysis, which is summarized inTABLES 3A-3F below. It turns out that the group of patients that is mostlikely to change from “good” to “poor” or “undefined” is the group ofmale (ex)smokers. Female non-smokers rarely change their class labelduring gefitinib treatment. If we assign a change in the classificationlabel from good to poor/undefined as indicative of emerging tumorresistance to EGFR-Is (as supported by the observed correlation toprogression, i.e. the drugs are no longer effective for tumor control),then VeriStrat constitutes the first molecular diagnostics of drugefficacy during treatment.

This finding is interesting as it is well understood that populationfactors like gender and smoking status are general prognostic markersfor NSCLC patients. One could tentatively conclude that the reason thatfemale non-smokers do well overall, and are good candidates for EGFR-Itreatment, because they do not develop resistance to EGFR-Is as thegroup of male and (ex) smokers.

TABLE 3A Subgroup Table for class label changes at progression for thesubgroup of (ex)smokers. The changes are significant with a p-value of0.0008. Good Poor Undefined Baseline 61 28 1 Progression 36 46 6

TABLE 3B Subgroup Table for class label changes at progression for thesubgroup of adenocarcinoma patients. The changes are on the borderlineof significance with a p-value of 0.084. Good Poor Undefined Baseline 4212 1 Progression 30 20 3

TABLE 3C Subgroup Table for class label changes at progression for thesubgroup of Squamous cell carcinoma patients. The changes are on theborderline of significance with a p-value of 0.1. Good Poor UndefinedBaseline 15 10 0 Progression  8 16 1

TABLE 3D Subgroup Table for class label changes at progression for thesubgroup of male patients. The changes are significant with a p-value of0.0007. Good Poor Undefined Baseline 57 27 0 Progression 35 42 1

TABLE 3E Subgroup Table for class label changes at progression for thesubgroup of patients who never smoked. The changes are insignificantwith a p-value of 0.6. Good Poor Undefined Baseline 13 3 1 Progression14 3 0

TABLE 3F Subgroup Table for class label changes at progression for thesubgroup of female patients. The changes are insignificant with ap-value of 0.2. Good Poor Undefined Baseline 18 5 2 Progression 16 9 0

In summary, the results presented in this disclosure confirm severalthings:

1) That the class label initially assigned to a patient tends to remainreasonably stable over time.

2) At disease progression, about 30% of the patients whose baselineclass label was “good” switch to a “poor” (or undefined) profiles. Thesechanges are statistically significant.

3) Certain subgroups of patients are more likely to exhibit a change inthe class label at progression, especially males and (ex) smokers, i.e.such patients become resistant to treatment with EGFR pathway targetingdrugs.

4) Intra-individual class label changes rarely occur during treatmentbefore progression. Thus, the use of the classification methods of thisdisclosure can be used in treatment efficacy monitoring for NSCLCpatients with EGFR pathway targeting drugs.

Example

Methods for how this invention can be practiced will now be described infurther detail. As an initial step, a baseline classification of aplasma or serum sample of the patient using mass spectrometry andclassification using a training set is obtained, in the manner describedin detail in our prior application published as U.S. 2007/0231921. Themethodology is shown in FIG. 3 described below. If the class label forthe patient sample is “good”, the result indicates that the patient islikely to benefit from a drug targeting the EGFR pathway, such asgefitinib, erlotinib or cetuximab. The patient is then prescribed one ofthese drugs.

During the course of treatment by administration of an EFGR-I drug, thepatient is monitored periodically to determine whether the class labelfor a serum sample of the patient has changed. A change from “good” to“poor” indicates that the patient may be developing a non-responsivenessto further administration of the EGFR-I drug. This monitoring isillustrated in flow chart form in FIG. 3. The monitoring, shown asprocess 300, is performed at periodic intervals, such as an interval ofbetween 30 and 100 days.

At step 302, a serum or plasma sample is obtained from the patient. Inone embodiment, the serum samples are separated into three aliquots andthe mass spectrometry and subsequent steps 304, 306 (including sub-steps308, 310 and 312), 314, 316 and 318 are performed independently on eachof the aliquots.

At step 304, the sample is subject to mass spectrometry. A preferredmethod of mass spectrometry is MALDI time of flight (TOF) but othermethods are possible. Mass spectrometry produces data points thatrepresent intensity values at a multitude of mass/charge (m/z) values,as is conventional in the art. In one example embodiment, the samplesare thawed and centrifuged at 1500 rpm for five minutes at four degreesCelsius. Further, the serum samples may be diluted 1:10, or 1:5, inMilliQ water. Diluted samples may be spotted in randomly allocatedpositions on a MALDI plate in triplicate (i.e., on three different MALDItargets). After 0.75 ul of diluted serum is spotted on a MALDI plate,0.75 ul of 35 mg/ml sinapinic acid (in 50% acetonitrile and 0.1%trifluoroacetic acid (TFA)) may be added and mixed by pipetting up anddown five times. Plates may be allowed to dry at room temperature. Itshould be understood that other techniques and procedures may beutilized for preparing and processing serum in accordance with theprinciples of the present invention.

Mass spectra may be acquired for positive ions in linear mode using aVoyager DE-PRO or DE-STR MALDI TOF mass spectrometer with automated ormanual collection of the spectra. Seventy five or one hundred spectraare collected from seven or five positions within each MALDI spot inorder to generate an average of 525 or 500 spectra for each serumspecimen. Spectra are externally calibrated using a mixture of proteinstandards (Insulin (bovine), thioredoxin (E. coli), and Apomyglobin(equine)).

At step 306, the spectra obtained in step 304 are subject to one or morepre-defined pre-processing steps. The pre-processing steps 306 areimplemented in a general purpose computer using software instructionsthat operate on the mass spectral data obtained in step 304. Thepre-processing steps 306 include background subtraction (step 308),normalization (step 310) and alignment (step 312). The step ofbackground subtraction preferably involves generating a robust,asymmetrical estimate of background in the spectrum and subtracts thebackground from the spectrum. Step 308 uses the techniques described inU.S. 2007/0231921 and U.S. 2005/0267689, which is incorporated byreference herein. The normalization step 310 involves a normalization ofthe background subtracted spectrum. The normalization can take the formof a partial ion current normalization, or a total ion currentnormalization, as described in our prior patent application U.S.2007/0231921. Step 312 aligns the normalized, background subtractedspectrum to a predefined mass scale, as described in U.S. 2007/0231921,which can be obtained from investigation of the training set used by theclassifier.

Once the pre-processing steps 306 are performed, the process 300proceeds to step 314 of obtaining integrated intensity values ofselected features (peaks) in the spectrum over predefined m/z ranges.Using the peak-width settings of a peak finding algorithm, thenormalized and background subtracted amplitudes may be integrated overthese m/z ranges and assigned this integrated value (i.e., the areaunder the curve between the width of the feature) to a feature. Forspectra where no peak has been detected within this m/z range, theintegration range may be defined as the interval around the average m/zposition of this feature with a width corresponding to the peak width atthe current m/z position.

At step 314, as described in our patent application published as US2007/0231921, the integrated intensity values of features in thespectrum is obtained at one or more of the following m/z ranges:

5732 to 57955811 to 58756398 to 646911376 to 11515114594 to 1159911614 to 1175611687 to 1183111830 to 1197612375 to 1252923183 to 2352523279 to 23622 and65902 to 67502.

In a preferred embodiment, values are obtained at least eight of thesem/z ranges, and more preferably at all 12 of these ranges. Thesignificance, and methods of discovery of these peaks, is explained inthe prior patent application publication U.S. 2007/0231921. It has beendiscovered that this set of peaks is not only useful for patientselection and monitoring of NSCLC patients, but also CRC patients andHNSCC cancer patients as well.

At step 316, the values obtained at step 314 are supplied to aclassifier, which in the illustrated embodiment is a K-nearest neighbor(KNN) classifier. The classifier makes use of a training set of classlabeled spectra from other patients. The application of the KNNclassification algorithm to the values at 314 and the training set isexplained in our patent application publication U.S. 2007/0231921. Otherclassifiers can be used, including a probabilistic KNN classifier orother classifier.

At step 318, the classifier produces a label for the spectrum, either“good”, “poor” or “undefined”. As mentioned above, steps 304-318 areperformed in parallel on three separate aliquots from a given patientsample. At step 320, a check is made to determine whether all threealiquots produce the same class label. If not, an undefined result isreturned as indicated at step 322. If all aliquots produce the samelabel, the label is reported as indicated at step 324.

If the label reported at step 324 is “good” it indicates that thepatient is likely to benefit from continued administration of the EGFRpathway targeting drug. If the label reported at step 324 is “poor” itindicates that the patient is likely developing a resistance ornon-responsiveness to treatment by such a drug.

It will be understood that steps 306, 314, 316 and 3118 are typicallyperformed in a programmed general purpose computer using software codingthe pre-processing step 306, the obtaining of integrated intensityvalues in step 314, the application of the KNN classification algorithmin step 316 and the generation of the class label in step 318. Thetraining set of class labeled spectra used in step 316 is stored inmemory in the computer or in a memory accessible to the computer.

From the foregoing, it will be appreciated that we have disclosed amethod of determining whether a NSCLC, HNSCC or CRC patient hasdeveloped non-responsiveness to treatment with a drug targeting the EGFRpathway, comprising the steps of:

a) obtaining a mass spectrum from a blood-based sample from the patient;

b) performing one or more predefined pre-processing steps on the massspectrum obtained in step a)(e.g., background subtraction, normalizationand spectral alignment);

c) obtaining integrated intensity values of selected features in saidspectrum at one or more predefined m/z ranges after the pre-processingsteps on the mass spectrum in step b) have been performed;

d) using the values obtained in step c) in a classification algorithmusing a training set comprising class-labeled spectra produced fromblood-based samples from other patients to identify the patient ashaving developed a non-responsiveness to treatment with the said drug.

Examples of the drug include gefitinib, erlotinib, or cetuximab orequivalent thereof (e.g., generic version or other drug which would beconsidered equivalent to these drugs).

Steps a)-d) are preferably performed periodically. For example they areperformed at intervals of between 30 and 100 days.

In another aspect, a method is disclosed of facilitating treatment of aNSCLC, HNSCC or CRC patient, comprising the steps of:

1) assigning a baseline class label for the patient using a classifieroperating on integrated intensity values at pre-defined m/z ranges inmass-spectral data obtained from a blood-based sample of the patient anda training set comprising class-labeled spectra from other patients, thebaseline class label indicating whether the patient is likely to benefitfrom administration of a drug targeting an epidermal growth factorreceptor pathway. If the baseline class label is “good” (or theequivalent), the patient would be likely to benefit and the drug isadministered to the patient; and

2) thereafter, while the patient is being treated with the drug, a)obtaining a mass spectrum of a blood-based sample of the patient, b)performing one or more predefined pre-processing steps on the massspectrum, c) obtaining integrated intensity values of selected featuresin said spectrum at one or more predefined m/z ranges after thepre-processing steps on the mass spectrum have been performed, and d)assigning a further class label for the sample using the classifier andthe training set, the further class label indicating whether the patienthas developed a non-responsiveness to the treatment with the said drug.This step 2) is preferably performed periodically over the course oftreatment, e.g., every 30, 60 or 100 days.

Variations from the particular details of the preferred embodimentsdisclosed are of course possible without departure from the scope of theinvention. All questions of scope are to be determined by reference tothe appended claims. The appended claims are further considered part ofthe present inventive disclosure.

1. A method of facilitating treatment of a colorectal cancer (CRC)patient comprising the steps of: 1) assigning a baseline class label forthe patient using a classifier operating on integrated intensity valuesat pre-defined m/z ranges in mass-spectral data obtained from ablood-based sample of the patient and a training set comprisingclass-labeled spectra from other patients, the baseline class labelindicating whether the patient is likely to benefit from administrationof a drug targeting an epidermal growth factor receptor pathway, whereinif the baseline class label is “good” (or the equivalent), the drug isadministered to the patient; and 2) thereafter, while the patient isbeing treated with the drug, a) obtaining a mass spectrum of ablood-based sample of the patient, b) performing one or more predefinedpre-processing steps on the mass spectrum, c) obtaining integratedintensity values of selected features in said spectrum at one or morepre-defined m/z ranges after the pre-processing steps on the massspectrum have been performed, and d) assigning a further class label forthe sample using the classifier and the training set, the further classlabel indicating whether the patient has developed a non-responsivenessto the treatment with the said drug.
 2. The method of claim 1, whereinstep 2) including sub-steps a)-d) are performed periodically during thecourse of treatment of the patient.
 3. The method of claim 1, whereinthe one or more pre-defined m/z ranges comprises one or more m/z rangesselected from the group of m/z ranges consisting of: 5732 to 5795 5811to 5875 6398 to 6469 11376 to 11515 11459 to 11599 11614 to 11756 11687to 11831 11830 to 11976 12375 to 12529 23183 to 23525 23279 to 23622 and65902 to
 67502. 4. The method of claim 1, wherein the steps a)-d) areperformed at intervals of between 30 and 100 days.